I am currently an Assistant Professor in Visual Information Processing and Learning (VIPL) group at the Institute of Computing Technology (ICT), Chinese Academy of Sciences (CAS). I received my Ph.D. from ICT, CAS in 2024, under the supervision of Prof. Xilin Chen. I also had close collaboration with Prof. Xiujuan Chai. My research interests mainly focus on human behavior analysis and understanding from sequential data, especially for gesture and sign language. I am also interested in exploring how to use data in deep learning more efficient. Feel free to reach out to me if you have an interest in the relevant topics.
🔥 News
- 2024.06: 🎉🎉 One paper on vision-language pre-training in SLT was accepted by ECCV 2024. Congratulations to Peiqi Jiao and the team!
- 2023.12: 🎉🎉 Successfully defended my PhD dissertation.
- 2023.10: 🎉🎉 One paper on keyframe selection in CSLR was accepted by Scientia Sinica Informationis 2023.
- 2023.10: Present the doctoral consortium “Alignment Constraints for Video-based Sign Language Understanding” at the workshop on Assistive Computer Vision and Robotics at ICCV23 [pdf] [workshop]
- 2023.07: 🎉🎉 One paper on co-occurrence signals in CSLR was accepted by ICCV 2023. Congratulations to Peiqi Jiao and the team!
- 2022.07: 🎉🎉 One paper on sequential representation learning was accepted by ECCV 2022.
📝 Publications
Deep Radial Embedding for Visual Sequence Learning
Yuecong Min, Peiqi Jiao, Yanan Li, Xiaotao Wang, Lei Lei, Xiujuan Chai, Xilin Chen
- RadialCTC constrains sequence features on a hypersphere while retaining the iterative alignment mechanism of CTC, which also provides a clear geometric interpretation for CTC
- RadialCTC controls the peaky behavior with a simple angular perturbation term
Visual Alignment Constraint for Continuous Sign Language Recognition
Yuecong Min, Aiming Hao, Xiujuan Chai, Xilin Chen
- VAC provides an efficient way to make CSLR models end-to-end trainable and is adopted as the baseline model by many recent works
- Two metrics to evaluate the contributions of the feature extractor and the alignment module
An Efficient PointLSTM for Point Clouds Based Gesture Recognition
Yuecong Min, Yanxiao Zhang, Xiujuan Chai, Xilin Chen
- PointLSTM can leverage long-term spatio-temporal relationships in irregular sequence data (e.g., point cloud) while preserving the spatial structure for irregular sequence recognition problem
- Evaluation results on 3D gesture recognition and action recognition show great potential for real-time applications
- Visual Alignment Pre-training for Sign Language Translation. Peiqi Jiao, Yuecong Min, Xilin Chen, accepted by ECCV, 2024.
- Adaptive Keyframe Selection for Continuous Sign Language Recognition. Yuecong Min, Xilin Chen, SCIENTIA SINICA Informationis, 2023.
- CoSign: Exploring Co-occurrence Signals in Skeleton-based Continuous Sign Language Recognition. Peiqi Jiao, Yuecong Min, Yanan Li, Xiaotao Wang, Lei Lei, Xilin Chen, International Conference on Computer Vision (ICCV), 2023.
- Self-Mutual Distillation Learning for Continuous Sign Language Recognition. Aiming Hao, Yuecong Min, and Xilin Chen, International Conference on Computer Vision (ICCV), 2021.
- Teaching Chinese Sign Language with A Smartphone. Yanxiao Zhang, Yuecong Min, Xilin Chen, Virtual Reality & Intelligent Hardware, 2021.
- FlickerNet: Adaptive 3D Gesture Recognition from Sparse Point Clouds. Yuecong Min, Xiujuan Chai, Lei Zhao, Xilin Chen, British Machine Vision Conference (BMVC), 2019.
🎖 Honors and Awards
- China National Scholarship for Ph.D., ICT, CAS, 2022
📖 Educations
- 2017.09 - 2024.1, I was a Ph.D. student at Institute of Computing Technology, CAS, under the supervision of Prof. Xilin Chen.
- 2013.09 - 2017.07, I was a college student in Shandong University, Weihai.
✒️ Academic Services
- Invited journal reviewer for IEEE TPAMI / IEEE TMM / IEEE TIP / PR …
- Invited conference reviewer for CVPR’22 /ACM MM’22 / ECCV’22 / CVPR’23 …
⚙️ Misc
- A summary of papers on gesture and sign language recognition.
- A simple tool to visualize the main keywords of accepted papers for the recent Computer Vision conferences
